feat: Implement data quality field contraint protos#366
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omirandadev wants to merge 4 commits into
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Manisha4
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Jun 15, 2026
| if len(proto.allowed_values) > 0: | ||
| kwargs["allowed_values"] = list(proto.allowed_values) | ||
| # `regex` is a plain string in proto3; empty == unset. | ||
| if proto.regex != "": |
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Saw the empty == unset note here, makes sense for proto3. Only nit: the validator still accepts regex="" and to_proto writes it, so you can create a value that silently reads back as None. Could reject empty regex in _regex_compiles to keep the two ends consistent.
Manisha4
reviewed
Jun 15, 2026
| allowed_values: Optional[List[str]] = None | ||
| regex: Optional[str] = None | ||
| unique: Optional[bool] = None | ||
| custom: Optional[Dict[str, str]] = None |
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Every other field validates at definition time (regex compiles, ranges in [0,1], etc.), but custom predicates are stored as-is, so a bad/empty predicate only fails at the next FE run. Could we reject empty predicate strings here? Maybe a non-empty check would catch the common typo early.
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What this PR does / why we need it:
Adds typed
FieldConstraintsandImputationproto messages plus matching Python/Pydantic surfaces so customers can declare per-column data quality rules and null-imputation strategies on aSparkSource. Constraints are stored on the source's proto and round-trip through the registry. The runtime that enforces them lives downstream in implementing feature engineering jobs that handle them.Which issue(s) this PR fixes:
Misc